# Advances in Quantum Deep Learning: An Overview

arXiv: https://arxiv.org/abs/2005.04316 · HTML: https://mlsanity.com/paper/2005.04316/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 21 of 21)

- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [Rapid training of quantum recurrent neural networks](https://mlsanity.com/paper/2207.00378/) (2022)
- [Quantum-Inspired Machine Learning: a Survey](https://mlsanity.com/paper/2308.11269/) (2023)
- [Quantum Bayesian Optimization](https://mlsanity.com/paper/2310.05373/) (2023)
- [A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification](https://mlsanity.com/paper/2201.01820/) (2022)
- [Toward Physically Realizable Quantum Neural Networks](https://mlsanity.com/paper/2203.12092/) (2022)
- [A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications](https://mlsanity.com/paper/2106.02964/) (2021)
- [Evolution strategies: Application in hybrid quantum-classical neural networks](https://mlsanity.com/paper/2205.08059/) (2022)
- [Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation](https://mlsanity.com/paper/2306.11297/) (2023)
- [Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers](https://mlsanity.com/paper/2102.08949/) (2021)
- [Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients](https://mlsanity.com/paper/2310.15128/) (2023)
- [Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models](https://mlsanity.com/paper/2408.00673/) (2024)
- [A Quantum Convolutional Neural Network Approach for Object Detection and Classification](https://mlsanity.com/paper/2307.08204/) (2023)
- [KirchhoffNet: A Scalable Ultra Fast Analog Neural Network](https://mlsanity.com/paper/2310.15872/) (2023)
- [Hybrid Quantum Neural Network in High-dimensional Data Classification](https://mlsanity.com/paper/2312.01024/) (2023)
- [A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity](https://mlsanity.com/paper/2402.15333/) (2024)
- [Deep learning-based variational autoencoder for classification of quantum and classical states of light](https://mlsanity.com/paper/2405.05243/) (2024)
- [Quantum Natural Stochastic Pairwise Coordinate Descent](https://mlsanity.com/paper/2407.13858/) (2024)
- [Distributed and Secure Kernel-Based Quantum Machine Learning](https://mlsanity.com/paper/2408.10265/) (2024)
- [Neural Architecture Search for Quantum Autoencoders](https://mlsanity.com/paper/2511.19246/) (2025)
- [Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics](https://mlsanity.com/paper/2608.00850/) (2026)

## Abstract (arXiv)

The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. In this work, we present an overview of advances in the intersection of quantum computing and deep learning by discussing the technical contributions, strengths and similarities of various research works in this domain. To this end, we review and summarise the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs). We also briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.